Abstract
Demand Response (DR) programs are essential for easing end-user demand on the power system, adding benefits across the power sector by reducing peak demand and power flow congestion. With the modernization of power grids, DR programs ensure the integration of Distributed Energy Resources (DER) in a controlled manner through Advanced Metering Infrastructure (AMI), which enables communication between grid operators, prosumers and consumers. However, the diversity of DR programs, the spread of DERs, the advent of prosumers, and the several types of power trading among system entities make network and market operation more complex. In this context, optimization methods have been widely applied in distribution grids and market operation, assisting in the decision-making of DER management, prosumer's and consumers' welfare, and DR program applications. This work will address the main market models comprising DR programs to assess opportunities in prosumers' decision-making with the help of optimization tools. Thus, different optimization techniques are introduced that have been addressed in the literature aiming at the application of market models, taking into account the pro -sumer framework. As a whole, this review paper aims to present the main perspectives of energy market models with demand-side management actions considering the prosumer design.

Abstract

Abstract
To provide a safe and fair-value health service that ensures quality, hospitals must provide efficient processes, trained and committed personnel, appropriate technology and a strategic platform which integrates these aspects effectively. At present, a broad set of tools and methodologies are available, associated to the reconfiguration of processes for enhancing efficiency and enabling excellence and sustainability. Of these, the most noteworthy are Lean and Six-Sigma methodologies. A literature review was performed covering the implementation of these methodologies in health services over the last 5 years. The aim was to determine the current approach in this sector and propose guidelines aligned with the future challenges and the needs of healthcare managers. The influence of team management strategies in the final project outcomes has also been addressed representing a novelty.

Abstract
There is a huge proliferation of digital products on the market today for both large enterprises and small businesses. Most of these companies have experienced the development of software products for the mobile market and have been faced with the major challenge of capturing the customer's attention. There is a great focus on making a great first impact and providing the audience with the best possible digital experience. Accordingly, issues related to usability, accessibility, and user experience are extremely relevant. This chapter addresses how these practices can be used in practice by building an app that offers car cleaning services. Several approaches based on building app interfaces that increase user engagement and retention levels are explored and discussed.

Abstract
The power system is facing a transition from its traditional centralized model to a more decentralized one, through the emergence of proactive consumers on the network, known as prosumers. This paradigm shift favors the emergence of new electricity market designs. Peer-to-Peer (P2P) based structures have been gaining prominence worldwide. In the P2P market, the prosumer assumes a more active role in the system, being able to directly trade its energy without the need for intermediaries. This paper contributes with a comprehensive overview of consumer-centric electricity markets, providing background on different aspects of P2P sharing, in particular the inclusion of peer preferences in the electricity trading process through product differentiation. A performance assessment of the different modeled preferences was carried out using key performance indicators (KPIs). Different user preferences under the product differentiation mechanism were simulated. The results demonstrate that consumer-centric markets increase the penetration of renewable energy sources into the network and tend to affect loads flexibility according to the renewable generation.

Abstract
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent work, evidence was shown that these approaches systematically present a lower predictive performance relative to simple statistical methods. In this work, we counter these results. We show that these are only valid under an extremely low sample size. Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows. The R code to reproduce all of our experiments is available at https://github.com/vcerqueira/MLforForecasting.